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This content will become publicly available on July 1, 2024

Title: Unifying Threats against Information Integrity in Participatory Crowd Sensing
This article proposes a unified threat landscape for Participatory Crowd Sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack types and goals, underscoring formal definition, their relevance and impact on the P-CS platform.  more » « less
Award ID(s):
2030611 2017289
NSF-PAR ID:
10434255
Author(s) / Creator(s):
;
Editor(s):
Kawsar, Fahim
Date Published:
Journal Name:
IEEE pervasive computing
ISSN:
1536-1268
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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